Given pairs of observed predictor vectors \(\mathbf {x}\in \mathbb {R}^{p}\)  and scalar outputs \(y \in \mathbb {R},\)  [1]}'s Bayesian additive regression trees (BART) model approximates the regression function \(f(\mathbf {x}) := \mathbb {E}[y \vert \mathbf {x}]\)  with a sum of binary regression trees.
BART often delivers accurate point estimates and well-calibrated uncertainty intervals around evaluations \(f(\mathbf {x})\)  without requiring users to (a) pre-specify the function al form of \(f\)  or (b) tune any hyperparameters.
BART's ease-of-use and generally excellent tuning-free estimation, prediction, and uncertainty quantification have made BART a popular “off-the-shelf” modeling tool.
